What are the Conditions of Riparian Ecosystems? Identifying Impaired Floodplain Ecosystems across the Western U.S. Using the Riparian Condition Assessment (RCA) Tool

Abstract

Environmental stressors associated with human land and water-use activities have degraded many riparian ecosystems across the western United States. These stressors include (i) the widespread expansion of invasive plant species that displace native vegetation and exacerbate streamflow and sediment regime alteration; (ii) agricultural and urban development in valley bottoms that decouple streams and rivers from their floodplains and reduce instream wood recruitment and retention; and (iii) flow modification that reduces water quantity and quality, degrading aquatic habitats. Here we apply a novel drainage network model to assess the impacts of multiple stressors on reach-scale riparian condition across two large U.S. regions. In this application, we performed a riparian condition assessment evaluating three dominant stressors: (1) riparian vegetation departure from historical condition; (2) land-use intensity within valley bottoms; and (3) floodplain fragmentation caused by infrastructure within valley bottoms, combining these stressors in a fuzzy inference system. We used freely available, geospatial data to estimate reach-scale (500 m) riparian condition for 52,800 km of perennial streams and rivers, 25,600 km in Utah, and 27,200 km in 12 watersheds of the interior Columbia River Basin (CRB). Model outputs showed that riparian condition has been at least moderately impaired across ≈70% of the streams and rivers in Utah and ≈49% in the CRB. We found 84% agreement (Cohen’s ĸ = 0.79) between modeled reaches and field plots, indicating that modeled riparian condition reasonably approximates on-the-ground conditions. Our approach to assessing riparian condition can be used to prioritize watershed-scale floodplain conservation and restoration by providing network-scale data on the extent and severity of riparian degradation. The approach that we applied here is flexible and can be expanded to run with additional riparian stressor data and/or finer resolution input data.

Full text

Environmental Management
https://doi.org/10.1007/s00267-018-1061-2
What are the Conditions of Riparian Ecosystems? Identifying
Impaired Floodplain Ecosystems across the Western U.S. Using the
Riparian Condition Assessment (RCA) Tool
William W. Macfarlane 1●Jordan T. Gilbert1●Joshua D. Gilbert1●William C. Saunders1,2 ●Nate Hough-Snee3●
Chalese Hafen1●Joseph M. Wheaton1,4 ●Stephen N. Bennett1,2,4
Received: 17 November 2017 / Accepted: 25 April 2018
© Springer Science+Business Media, LLC, part of Springer Nature 2018
Abstract
Environmental stressors associated with human land and water-use activities have degraded many riparian ecosystems across
the western United States. These stressors include (i) the widespread expansion of invasive plant species that displace native
vegetation and exacerbate streamflow and sediment regime alteration; (ii) agricultural and urban development in valley
bottoms that decouple streams and rivers from their floodplains and reduce instream wood recruitment and retention; and (iii)
flow modification that reduces water quantity and quality, degrading aquatic habitats. Here we apply a novel drainage
network model to assess the impacts of multiple stressors on reach-scale riparian condition across two large U.S. regions. In
this application, we performed a riparian condition assessment evaluating three dominant stressors: (1) riparian vegetation
departure from historical condition; (2) land-use intensity within valley bottoms; and (3) floodplain fragmentation caused by
infrastructure within valley bottoms, combining these stressors in a fuzzy inference system. We used freely available,
geospatial data to estimate reach-scale (500 m) riparian condition for 52,800 km of perennial streams and rivers, 25,600 km
in Utah, and 27,200 km in 12 watersheds of the interior Columbia River Basin (CRB). Model outputs showed that riparian
condition has been at least moderately impaired across ≈70% of the streams and rivers in Utah and ≈49% in the CRB. We
found 84% agreement (Cohen’sĸ=0.79) between modeled reaches and field plots, indicating that modeled riparian
condition reasonably approximates on-the-ground conditions. Our approach to assessing riparian condition can be used to
prioritize watershed-scale floodplain conservation and restoration by providing network-scale data on the extent and severity
of riparian degradation. The approach that we applied here is flexible and can be expanded to run with additional riparian
stressor data and/or finer resolution input data.
Keywords Conservation planning ●Riparian restoration ●Watershed condition assessment ●Riparian degradation ●
Floodplain ecology ●Columbia River Basin ●Utah
Introduction
Floodplain riparian ecosystems form the ecotone between
streams and rivers and the terrestrial landscapes they con-
nect, providing important ecosystem services for humans
(Castellarin et al. 2011; DeLaney 1995; Lowrance et al.
1997; Mander et al. 2005) and vital habitat for numerous
plant and animal species (Baron et al. 2003; Naiman and
and Decamps 1997; Naiman et al. 2000). Although flood-
plain riparian ecosystems (herein floodplain ecosystems)
represent a small portion of earth’s surface area, they pro-
vide a disproportionately large amount of ecosystem ser-
vices (Costanza et al. 2016; Tockner and Stanford 2002).
Intact floodplains and robust riparian vegetation attenuate
*William W. Macfarlane
wally.macfarlane@usu.edu
1Department of Watershed Sciences, Utah State University, 5210
Old Main Hill, Logan, UT 84322-5210, USA
2Eco Logical Research, Inc., Providence, UT 84332, USA
3Meadow Run Environmental, LLC, Leavenworth, WA 98826,
USA
4Anabranch Solutions, LLC, Newton, UT 84327, USA
Electronic supplementary material The online version of this article
(https://doi.org/10.1007/s00267-018-1061-2) contains supplementary
material, which is available to authorized users.
1234567890();,:
1234567890();,:

floods (Tabacchi et al. 2000; Woltemade and Potter 1994),
and play vital roles in cycling nutrients and organic matter
from adjacent landscapes (Lowrance 1998), improving
downstream water quality. In addition to buffering streams
and rivers, floodplain ecosystems also provide recreational
opportunities and essential land use functions (DeFries et al.
2004; Gren et al. 1995). Similarly, these ecosystems support
especially high biodiversity (Tockner and Stanford 2002;
Ward et al. 1999) and are critically important for many
wildlife species that are of great conservation concern
(Golet et al. 2008; Kus 1998) or high cultural or economic
value (Jeffres et al. 2008). However, despite their impor-
tance, riparian ecosystems, and the streams and rivers that
traverse them are among the world’s most heavily degraded
landscapes (Dudgeon et al. 2006; Opperman et al. 2009).
Habitat degradation and biological invasions are the two
leading causes of ecosystem alteration and biodiversity loss
worldwide (Fahrig 2003; Pimentel et al. 2000; Vitousek
et al. 1997), and these stressors’impacts are particularly
evident in floodplain ecosystems (Shafroth et al. 2002;
Stohlgren et al. 1998; Tockner and Stanford 2002).
Removal of native riparian vegetation, replacement of
floodplain habitats with impervious surfaces, and alteration
of floodplain topography by transportation infrastructure
(Blanton and Marcus 2013; Hall et al. 2007) alter surface
water drainage patterns (May et al. 1999) and hydrologic
regimes (Booth and Jackson 1997). These hydrologic
impacts subsequently alter stream channel geometry
(Taniguchi and Biggs 2015) and water chemistry (Carpenter
et al. 1998; Liess and Schulz 1999; Schoonover et al. 2005).
Riparian forest clearing for agriculture also reduces stream
shading (Allan 2004; Klemas 2014), increases stream tem-
perature (Beschta and Taylor 1988), and removes riparian
sources of large woody debris (Gurnell et al. 1995).
In the western U.S., non-native riparian vegetation, like
tamarisk (Tamarix spp.) and Russian olive (Elaeagnus spp.
e.g., Shafroth et al. 2002; Stromberg et al. 2007), further
alters riparian habitat structure, terrestrial–aquatic linkages
(Roon et al. 2014,2016), and aquatic communities (Stella
et al. 2013). Additionally, habitat degradation and biologi-
cal invasions occur in tandem with larger, global phenom-
ena like climate-induced changes to rainfall, runoff, and
streamflow (Galloway et al. 2004; Ormerod et al. 2010; Poff
et al. 2002). Moreover, biocontrol efforts undertaken to
control invasive woody vegetation can have unforeseen
consequences. For example, since the tamarisk beetle
(Diorhaba spp.) was released in 2001, it has caused wide-
spread tamarisk defoliation and decline throughout the
Colorado River Basin (Bloodworth et al. 2016). This
reduction in tamarisk cover has helped restore habitat for
some native shrub and tree species. However, where
tamarisk has declined and hydrology has remained altered,
limited woody vegetation has replaced tamarisk, reducing
habitat abundance for wildlife species who rely on tamarisk
for habitat (Bloodworth et al. 2016). Nevertheless, the
cumulative effects of biotic and anthropogenic impacts have
resulted in significantly different riparian and instream
habitats than those in which many native fish and riparian
fauna evolved (May et al. 1999). These alterations reduce
native species abundance and diversity (Rolls and
Arthington 2014; Royan et al. 2015) and decouple impor-
tant linkages between biological communities and their
habitats (Foley et al. 2005; Hooper et al. 2005).
Floodplain degradation associated with riparian vegeta-
tion change (Macfarlane et al. 2016a), intensive land use
(Allan 2004), and transportation infrastructure (Blanton and
Marcus 2013; Forman et al. 2002) is common across wes-
tern North America, yet regional assessments of how these
stressors align to adversely impact reach-scale riparian
condition are rare. We attribute this to several factors: (1)
methodological limitations of combining multiple stressors
at the regional scale (Goetz 2006); (2) lack of confidence in
using nationally available land cover data to assess riparian
condition (Johansen and Phinn 2006); and (3) the cost
prohibitive nature of using high-resolution imagery at large
spatial scales (Salo et al. 2016).
Consequently, riparian ecosystem degradation studies
often examine only small landscapes or isolated causes of
degradation (e.g. Hough-Snee et al. 2013). This lack of
comprehensive riparian condition data challenges resource
managers tasked with restoring large floodplain ecosystems,
often entire watersheds, leaving them with only locally
available data on how and where multiple stressors have
impacted these ecosystems.
In an effort to improve river and riparian management,
valley bottom mapping (Gilbert et al. 2016) and reach scale
vegetation change inventories have been produced for all
perennial streams’valley bottoms within the state of Utah
and across several interior Columbia River Basin (CRB)
watersheds (Macfarlane et al. 2016a). While Macfarlane
et al. (2016a) cataloged the extent to which valley bottoms
have been impacted by non-native vegetation and upland
encroachment, their analysis did not directly account for the
impacts of land-use intensity and floodplain fragmentation
on riparian ecosystems. Given the importance of functional
riparian ecosystems to fish and wildlife populations, the
enormous extent of riparian degradation across the western
U.S. (Kauffman et al. 1997), and a general lack of riparian
condition information in many regions, riparian assessments
that account for these additional stressors are increasingly
important for sustainable watershed management.
We developed a spatially explicit framework for asses-
sing riparian condition that can be used for reach-level
conservation and restoration planning across broad geo-
graphic areas (Harris and Olson 1997). Our objectives were
to (1) develop a generic model that can use either relatively
Environmental Management

coarse or high-resolution land cover, transportation infra-
structure, and land-use data to assess riparian condition, and
(2) demonstrate the model’s utility in a western U.S. con-
text, applying the model using relatively coarse, nationally
available data to assess riparian condition across a large
range of physiographic settings in both the state of Utah and
the CRB, USA.
Methods
Study Locations
We focused the riparian condition assessment (RCA) tool
on perennial streams across Utah (≈25,600 km), and within
12 CRB watersheds that are of fisheries management and
restoration concern (Fig. 1). Watersheds within the CRB
included the John Day and Upper Grande Ronde in Oregon,
the Tucannon, Entiat, Wenatchee, and Asotin in Washing-
ton, and the Upper Salmon, Yankee Fork, Lemhi, Lochsa,
Lower Clearwater, and South Fork Clearwater in Idaho
(totaling ≈27,200 km of streams). These watersheds occur in
the Columbia Plateau Physiographic Province (Vigil et al.
2000) which includes a diverse range of mountains, pla-
teaus, canyons, and rolling hills (Fig. 1). The CRB effort
was part of the Columbia Habitat Monitoring Program
(CHaMP; http://champmonitoring.org) which tracks the
status and trend of anadromous salmonid habitat throughout
the CRB (Bouwes et al. 2011).
Utah is a physiographically diverse landscape covering
219,808 km2that range from alpine meadows to desert
canyons, with riparian conditions varying widely based on
physical setting and management history. The state of Utah
includes three primary physiographic regions, each with
unique topographic, geologic, and geomorphic character-
istics: the Colorado Plateau, the Basin and Range, and the
Middle Rocky Mountains (Vigil et al. 2000). Utah’s ele-
vation ranges from 664 m at Beaver Dam Wash in south-
western Utah to 4123 m on King’s Peak in the Uinta
Mountains. Utah provided an ideal range of landscapes
across which we could test the robustness of an RCA
approach.
Differentiating Valley Bottom Setting
By definition, a valley bottom is composed of active and
inactive stream channels and their floodplains (Fryirs et al.
2016; Wheaton et al. 2015). Fryirs and Brierley (2013) used
the position of the channel on the valley bottom floor to
define ranges of confinement that differentiate valley bot-
tom settings. This includes confined, partly confined and
laterally unconfined. Differentiation of these valley bottom
settings reflects the position of the channel relative to the
valley bottom margin, indicating how often and over what
distance the channel impinges on that margin. In our clas-
sification, a confined valley settings is where the channel
abuts a confining margin greater than 85% of its length, a
partly confined valley setting is where the channel abuts a
confining margin 10–85% of its length, and a laterally
unconfined valley setting is where the channel abuts a
confining margin less than 10% of its length.
In our RCA we treated streams with confined valley
bottom settings differently than streams with partly confined
and unconfined valley bottom settings (hereafter both
referred to as unconfined) because confined streams lack a
floodplain (Wheaton et al. 2015), have limited space to
grow riparian vegetation, and are difficult to detect from
medium-resolution satellite imagery (Macfarlane et al.
2016a). Consequently, confined reaches were assigned to
one of two categories: confined-impacted or confined-
unimpacted. A reach was considered impacted if there was a
detectible reduction in vegetation or conversion of land or
transportation infrastructure within the valley bottom.
To separate confined from unconfined streams within the
model’s automated workflow, we used valley bottom width
as a proxy for confinement, defining an adjustable valley
bottom-width threshold parameter that represents valley
bottom width in meters (which was calculated automatically
for each reach). To calibrate the valley bottom-width
threshold, we calculated valley bottom confinement using
the approach outlined in Fryirs et al. (2016) and the con-
finement tool developed in O'Brien et al. (In Revision). For
each watershed, the total length of confined streams was
calculated using the confinement tool. These length values
were used to calibrate the valley bottom-width threshold.
Specifically, for each watershed within the study area, the
valley bottom-width threshold was adjusted until the
resulting stream lengths matched the confined streams
lengths as calculated by the confinement tool.
Riparian Condition Assessment
The RCA tool identifies riparian condition across valley
bottoms. We split valley bottoms into a series of Thiessen
polygons with centroids located at the midpoint of each
500-m stream segment (Fig. 2). Thiessen polygons were
chosen for this process because their geometric properties
guarantee that all points within a polygon are closer to that
polygon’s centroid than to any other polygon (Esri 2016b).
This ensures that land cover and land use adjacent to the
reach are attributed to the correct stream segment, even
when working with irregular planform geometries and
valley bottoms.
Riparian condition was summarized in the resulting
analysis polygons (Fig. 2) using an algorithm based on lines
of evidence that include: (1) riparian vegetation departure
Environmental Management

(RVD) from historic condition, (2) land-use intensity, and
(3) impediments to floodplain accessibility caused by
transportation infrastructure (e.g., raised grades; Blanton
and Marcus 2013). Each drainage network segment was
attributed with continuous values for each line of evidence.
The lines of evidence were combined using an FIS to
Fig. 1 Study locations within the
state of Utah and 12 interior
Columbia River Basin
watersheds of fisheries
management concern. These are
mapped over US Environmental
Protection Agency Level III
Ecoregions for additional
context
Environmental Management

Fig. 2 Conceptual diagram of riparian condition assessment (RCA)
tool showing how midpoints of a drainage network (a) are used to
generate Thiessen polygons (b). Riparian vegetation departure index
outputs (c) are combined with land-use intensity (d) and floodplain
accessibility outputs (e) within a Fuzzy Inference System (f) to pro-
duce a segmented drainage network containing riparian condition
assessment scores (g)
Environmental Management

collectively estimate riparian condition based on a linguis-
tic, expert-based rule system (Fig. 2).
RVD Index
To assess riparian vegetation condition, we used the RVD
index (Macfarlane et al. 2016a). The RVD index calculates
riparian vegetation’s departure from its historic condition as
the ratio of current vegetation cover to estimated historic
riparian vegetation cover. Both existing and historic vege-
tation that occurred as native riparian vegetation were coded
as ‘1’while invasive and upland classes were coded as ‘0.’
For each polygon, the mean vegetation value was calculated
which represents the proportion of native riparian cover
within each polygon. The area of native riparian cells,
within the analysis polygons, modeled in the historic
vegetation input was used as the denominator in the RVD
ratio, and the area of native riparian cells modeled in the
existing vegetation input was used as the numerator. Low
values (closer to 0) signify larger departures from historic
riparian vegetation condition whereas high values (closer to
1) signify small departures.
Assessment of Land-Use Intensity
We classified land-use intensity along a continuum from
zero to one where one is highly altered and zero is unaltered
using 2012 LANDFIRE EVT data (Table S1). Urbaniza-
tion, a land use that often dramatically and permanently
alters riparian ecosystems by covering floodplains with
impervious surfaces, corresponds to a land-use intensity
score of one (highly altered). Agriculture, which modifies
floodplain vegetation and disturbance regimes, corresponds
to a land-use intensity score of 0.33 to 0.66, depending on
the intensity (0.33 for pastoral use; 0.66 for row crop).
Areas that have no defined land-use were scored as zero
(unaltered). To attribute input network segments with a
land-use intensity value, we calculated the mean of land-use
intensity values for all cells within each analysis polygon,
resulting in a continuous value between zero and one that
was attributed to the corresponding drainage network
segment.
Assessment of Floodplain Accessibility
The RCA tool is designed to characterize floodplain
accessibility similar to Blanton and Marcus (2013), using a
transportation network layer that includes roads and rail-
roads as line features. We overlaid the transportation net-
work on the valley bottom polygon and split the polygon at
each location where a road or railroad occurred. These splits
separated the valley bottom into portions where the river
has the potential to inundate the floodplain at flood stages
and portions where the river’s access to the floodplain has
been eliminated or severely reduced by elevated railroad
and road grades. It is possible to extend the inputs for this
analysis with other infrastructure like levees, but we
excluded these from our analysis due to lack of nationally
consistent data. We generated the floodplain accessibility
analysis automatically using a geoprocessing method and
visually inspected results to ensure that all disconnected
areas were identified. We made additional manual splits
where lateral connectivity was misclassified by automated
geospatial analyses (Figure S2). For each analysis polygon,
we calculated the proportion of floodplain that is accessible
by the river channel as a ratio from zero (completely dis-
connected) to one (completely connected), and the corre-
sponding drainage network segment was attributed with that
value. The specific geoprocessing steps are described in
Appendix A.
Fuzzy Inference Systems to Score Riparian Condition
We used an FIS to combine the three lines of evidence to
estimate riparian condition over our study areas’drainage
networks (Fig. 3). The FIS provided a consistent and
repeatable framework for combining continuous variable
inputs to produce a continuous output. Categorical ambi-
guity and uncertainty among categories were explicitly
accounted for using fuzzy logic and by representing all
inputs and outputs as continuous variables with overlapping
membership functions for each category (Openshaw 1996;
Zadeh 1996). The FIS also allowed for ‘computing with
words,’whereby the three lines of evidence were mathe-
matically combined based on an expert-based rule system
(Table 1) using continuous numeric inputs that provided
continuous numeric outputs (Adriaenssens et al. 2003; Klir
and Yuan 1995). The FIS framework is also flexible and
expandable and can easily accommodate additional lines of
evidence for evaluating floodplain condition if such data are
available.
Within the FIS RVD, land-use intensity, and floodplain
accessibility scores were divided into categories. RVD
scores were split into four categories: large, significant,
minor, and negligible departure, under the framework of
(Macfarlane et al. 2016a). Both land-use intensity and
floodplain accessibility scores were split into three cate-
gories: low, moderate, and high. For each combination of
input category scores, a corresponding rule was created to
determine the output value range and associated categories
(Table 1). The range of output values was split into five
different categories of riparian condition: very poor, poor,
moderate, good, and intact (Fig. 3). For each input stream
segment, membership in each output category was calcu-
lated, and a final value attributed to the segment, using the
centroid defuzzification method (Mathworks 2017).
Environmental Management

Case Study Application and Validation
Case study data inputs
Segmented drainage network For our analyses we trim-
med the U.S. Geological Survey (USGS) National Hydro-
graphy Dataset (NHD), a cartographically derived 1:24,000
drainage network (USGS 2016), to perennial streams and
rivers (Table 2). We segmented the resulting perennial
drainage network longitudinally into 500 m long segments.
This was a reasonable length along which to sample 30 m
LANDFIRE land cover and land-use data and floodplain
accessibility. The 500 m reach length used here is also an
ideal resolution for conservation and restoration planning at
large spatial scales (Wheaton et al. 2017).
Valley bottom polygon We used the Valley Bottom
Extraction Tool (V-BET; Gilbert et al. 2016) with addi-
tional manual editing to delineate valley bottoms across the
study areas. V-BET requires three inputs: a digital elevation
model (DEM), a drainage network, and a flow accumulation
raster in which the value for each cell represents the
upstream drainage area (in km2). For this regional applica-
tion, we used USGS National Elevation Data (NED) 10 m
DEMs (Gesch et al. 2009) and NHD 1:24,000 scale dataset
(USGS 2016) as the drainage network. V-BET is based on
the assumptions that: (1) valley bottom width is a function
of upstream drainage area, with wider valley bottoms cor-
responding, crudely, to larger upstream drainage area
(Montgomery 2002; Nardi et al. 2006); (2) the average
slope of a valley bottom is related to upstream drainage
area; the larger the drainage area, the flatter the valley
bottom (McNamara et al. 2006; Montgomery 2001;
Schorghofer and Rothman 2002; Tucker and Bras 1998;
Willgoose et al. 1991); and (3) valley bottoms are relatively
flat areas with margins often defined by abrupt changes in
slope (Gallant and Dowling 2003).
In our application areas, streams with drainage area of
less than 25 km2were generally confined headwater
streams, those streams with drainage area in the
25–250 km2range were in a transition zone where valleys
widened and slopes decreased and those with drainage area
greater than 250 km2were generally larger rivers or
tributaries in alluvial valleys. Following the aforementioned
assumptions, V-BET delineates valley bottoms for these
different types of river reaches distinguished by drainage
area using varying thresholds for valley width and slope.
The larger rivers in alluvial valleys are delineated using
higher maximum valley width and lower slope thresholds,
whereas the valley bottoms of confined headwater reaches
are delineated using narrow maximum width and relatively
higher slope thresholds.
RVD index layer We calculated RVD from historic con-
dition using the RVD index (Macfarlane et al. 2016a). In
this application, Landsat imagery classification of existing
land cover (LANDFIRE EVT; LANDFIRE 2016a) and a
modeled estimate of pre-European settlement land cover
(LANDFIRE BpS; LANDFIRE 2016b) were used to char-
acterize riparian vegetation condition at a given 500 m
reach. LANDFIRE EVT vegetation map units are a mixture
of the following: ecological systems (defined as “groups of
vegetative associations that tend to co-occur within
Fig. 3 Fuzzy Inference System for riparian condition assessment
(RCA) tool. This shows the specification of fuzzy membership func-
tions with overlapping values for categorical descriptors in inputs and
outputs
Environmental Management

landscapes with similar ecological processes, substrates,
and environmental gradients”(Comer et al. 2003)), aggre-
gations of ecological systems for LANDFIRE purposes
(e.g. riparian systems or sparsely vegetated systems)
(Rollins 2009), and US National Vegetation Classification
alliances (Grossman et al. 1998). For example, the Rocky
Mountain Subalpine-Montane Riparian Shrubland class
consists of montane to subalpine riparian shrublands
occurring as narrow bands of shrubs lining streambanks and
alluvial terraces in narrow to wide, low-gradient valley
bottoms. The dominant shrubs include Alnus incana, Betula
glandulosa, Betula occidentalis, Cornus sericea, Salix
bebbiana, Salix boothii, Salix brachycarpa, Salix drum-
mondiana, Salix eriocephala, Salix geyeriana, Salix mon-
ticola, Salix planifolia, and Salix wolfii(http://explorer.na
tureserve.org/servlet/NatureServe?searchSystemUid=
ELEMENT_GLOBAL.2.722841). Although used primarily
for wildland fire behavior mapping, LANDFIRE map units
were also designed to be useful for applications such as
habitat analysis and sustainable natural resource planning
(Rollins 2009). We chose LANDFIRE data because of the
thorough national coverage, consistent collection methods
and accessible documentation.
Land-use layer We used the 2012 LANDFIRE EVT layer
to derive a land-use intensity layer (see above).
Manually created floodplain connectivity layer Transpor-
tation layers from the TIGER dataset (US Census Bureau
2016) were used to fragment the associated floodplains of
the valley bottoms within our study areas.
Accuracy assessment analysis
A critical component of any geospatial modeling exercise is
a rigorous, ground-based accuracy assessment. Because
RCA outputs are summarized in an ordinal-scale that is
based on a composite score, we chose to validate the
Table 1 Rule table for three
input fuzzy inference system
that models riparian condition
using riparian vegetation
departure, land-use intensity
within the valley bottom, and
floodplain accessibility due to
transportation infrastructure
If Inputs Output
Riparian vegetation
departure
Land-use
intensity
Floodplain
accessibility
Riparian
condition
Rules 1 Large & Low & Low , then Poor
2 Large & Low & Moderate , then Poor
3 Large & Low & High , then Moderate
4 Large & Moderate & Low , then Poor
5 Large & Moderate & High , then Poor
6 Large & High & Low , then Very Poor
7 Significant & Low & Low , then Moderate
8 Significant & Low & Moderate , then Moderate
9 Significant & Low & High , then Good
10 Significant & Moderate & Low , then Poor
11 Significant & Moderate & High , then Moderate
12 Significant & High & Low , then Poor
13 Minor & Low & Low , then Moderate
14 Minor & Low & Moderate , then Good
15 Minor & Low & High , then Intact
16 Minor & Moderate & Low , then Moderate
17 Minor & Moderate & High , then Moderate
18 Minor & High & Low , then Poor
19 Negligible & Low & Low , then Moderate
20 Negligible & Low & Moderate , then Good
21 Negligible & Low & High , then Intact
22 Negligible & Moderate & Low , then Moderate
23 Negligible & Moderate & High , then Good
24 Negligible & High & Low , then Poor
25 Any value & Moderate & Moderate , then Moderate
26 Any value & High & Moderate , then Poor
27 Any value & High & High , then Moderate
Environmental Management

component model inputs (RVD index values, land-use
intensity scores, and floodplain fragmentation percentage)
rather than the composite output scores. We validated our
model using a field accuracy assessment of: (1) existing
vegetation, (2) land-use type and intensity within the
valley bottom, (3) percentage of floodplain accessible to
the river, and (4) types of transportation infrastructure
present in the valley bottom. We validated RCA inputs in
the Weber River (Utah) and Tucannon River (Washing-
ton) watersheds. We selected validation sites within the
Weber River watershed using a stratified sampling
approach, constrained by public access and quality of
vantage point. In the Tucannon watershed, validation sites
were selected using a systematic survey, stratified by
USEPA Level 4 Ecoregions and whether a polygon
occurred in the mainstem river or tributarystreams.We
conducted systematic, road-based surveys assessing road
access and how well riparian extent and composition
could be assessed from each potential vantage point every
1-km along roadways that traversed rivers and streams of
the watershed.
To validate condition, the framework and rule table of
RCA was used, but with the collected field data informing
input values rather than the remotely sensed data used in the
analyses. We validated floodplain accessibility and land-use
intensity using field data to determine input categories. For
example, if field data showed that the valley bottom was
used as a pasture for livestock grazing, the segment was
attributed with moderate land-use intensity, whereas if there
was no land-use observed in the valley bottom, the segment
was attributed with low land-use intensity. The RVD out-
puts were validated independently (Macfarlane et al.
2016a), and as such modeled RVD values were used in lieu
of field data. After we attributed every segment with mod-
eled values for RVD and field values for land-use intensity
and floodplain accessibility, we then applied the rule set
used in the automated model to determine field data based
riparian condition for each segment. Finally, we directly
compared condition based on field validation data to con-
dition based on remotely sensed information. Cohen's kappa
statistic (Cohen’sĸ) was used to measure agreement
between modeled condition and field-based observations
because it accounts for chance agreement and is more
robust and conservative than an overall error rate (Con-
galton 1991).
Results
Confined Valley Settings
In Utah, nearly half (45%) of the states’perennial drainage
network was classified as confined valley settings (i.e., those
lacking floodplains), consisting predominantly of headwater
streams concentrated in mountainous portions of the state
(Figs. 4and 5and Table 3). Of the confined streams across
the state, 26% (4,104 of 15,539 km) were classified as
impacted vs. 74% (11,436 of 15,539 km) classified as
unimpacted (Table 3). Similarly, the majority (64%) of the
CRB watersheds perennial drainage network was classified
as confined consisting of mostly headwater streams con-
centrated in the mountainous portions of the region (Figs. 6
and 7and Table 4). Of the confined streams across the CRB,
17% (2891 of 17,319 km) were classified as impacted vs.
83% (14,428 of 17,319 km) were classified as unimpacted
(Table 4).
Region-Wide Results
Utah-Wide Application
Across Utah, the RCA tool showed that roughly 70% of
unconfined valley bottoms were in moderate to very poor
riparian condition (Fig. 4and Table 5). Floodplains of large
alluvial rivers where agricultural and urban land uses are
common were frequently in poor to very poor condition
(Fig. 4). In contrast, intact and good condition were com-
mon in floodplain ecosystems along large to medium-sized,
rivers in more remote areas of the state such as the Colorado
and Green Rivers, where transportation infrastructure and
intensive land uses are not common. Moderate condition
(41%) floodplains occurred throughout the state, often
corresponding to rural land use common along these river
corridors.
Table 2 Input data used to represent the lines of evidence of riparian condition assessment (RCA) tool
Input data Criteria Source
Riparian Vegetation Departure (RVD) index
output
Riparian vegetation condition http://www.sciencedirect.com/science/article/pii/
S0301479716308489
LANDFIRE 2012 (EVT) Land-use intensity LANDFIRE land cover data http://www.landfire.gov/
Roads Transportation infrastructure TIGER https://www.census.gov/geo/maps-data/data/tiger.html
Railroads Transportation infrastructure TIGER https://www.census.gov/geo/maps-data/data/tiger.html
Valley Bottom Extraction Tool (V-BET)
output
Valley bottom delineation http://www.sciencedirect.com/science/article/pii/
S0098300416301935
Environmental Management

Modeled riparian condition was slightly worse in Utah’s
western portion, including the Northern Basin and Range/
Wyoming Basin and Central/Mojave Basin and Range
Ecoregions compared to Utah’s central (the Wasatch and
Uinta Maintains) and eastern portions (the Colorado Pla-
teaus/Southern Rockies; Fig. 8). The Central/Mojave Basin
and Range Ecoregion, which has experienced widespread
urbanization along the Wasatch Front, exhibited the highest
proportion of very poor condition floodplains (8%; Fig. 8).
The Northern Basin and Range/Wyoming Basin had the
largest percentage of poor condition floodplains (42%),
coinciding with high intensity agriculture. The Wasatch and
Uinta Mountains and Colorado Plateaus/Sothern Rockies
Ecoregions had similar riparian conditions overall, but
degradation in each ecoregion was driven by different fac-
tors. In the Wasatch and Uinta Mountains, agriculture,
roads, and urbanization had the greatest impacts. In con-
trast, in the Colorado Plateau/Southern Rockies, invasive
riparian vegetation had the largest impact on riparian
condition.
Fig. 4 Map showing riparian condition assessment (RCA) tool outputs across the state of Utah
Environmental Management

CRB Watershed Application
Across the 12 CRB watersheds, the RCA model suggests
that just under half (49%) of riparian ecosystems are in
moderate to very poor condition (Fig. 6;Table6). As in
Utah, the RCA tool illustrated spatially variable patterns
of riparian condition within CRB watersheds. Floodplains
in very poor condition were rare (only 1%) and isolated to
only the most developed urban areas (Fig. 6). Poor con-
dition floodplains were uncommon (14%), and were evi-
dent only along large alluvial rivers where agricultural
and urban land uses are common (Fig. 6). Moderate
condition floodplains (34%) were the most widespread
category in the CRB, and were found interspersed
Fig. 5 Pie chart showing riparian condition assessment (RCA) tool outputs for all streams by US Environmental Protection Agency Level III
Ecoregions in Utah
Table 3 Summary of the Utah statewide riparian condition
assessment (RCA) tool for all streams by category
Riparian condition
assessment
Stream length (km) % of drainage
network
Confined-impacted 4103.8 16
Confined-unimpacted 11,436.2 45
Very poor 211.7 1
Poor 2839.4 11
Moderate 4108.4 16
Good 1917.9 7
Intact 1040.6 4
Total 25,658
Environmental Management

throughout the watersheds (Fig. 6). About half (51%) of
the floodplains were found to be either intact (31%)orin
good (20%) condition (Fig. 9and Table 6). Watersheds
with the best condition were the Lochsa, Entiat, and
Yankee Fork (Fig. 9), all relatively remote watersheds that
lack urban and intensive agriculture land use and have
only limited roads. The Lemhi and Tucannon were the
most impacted watersheds (Fig. 9). The Tucannon
watershed is dominated by intensive agriculture, which
has heavily impacted riparian areas. The current riparian
corridor consists primarilyofonlynarrowstreamside
bands of cottonwood (Populus trichocarpa)andalder
(Alnus spp.).
Validation
For ground truthing we surveyed 31 analysis polygons in
the Weber watershed (Figure S4) and 61 analysis polygons
in the Tucannon watershed (Figure S5). Error matrices
were constructed from field assessments of riparian con-
dition, derived from observations of transportation infra-
structure and land-use intensity. Our model estimates of
condition indicated a high overall level of agreement
between data sources. For all streams we identified an
overall map accuracy of 87% based on the 92 analysis
polygons (Table 7). The calculated Cohen’sĸwas 0.87.
Using Cohen’sĸ,one indicates full agreement and zero
indicates complete disagreement between modeled and
measured values. Thus, a 0.87 indicates an almost perfect
agreement (Landis and Koch 1977) between modeled and
field-based data.
The high accuracy when considering all streams may
result from the simplicity of the binary categorization as
confined-unimpacted or confined-impacted of confined
streams. Therefore, we evaluated the accuracy of polygons
containing only unconfined stream segments with flood-
plains (n=71). For this subset, the overall map accuracy
was 84%, and Cohen’sĸwas 0.79 (Table 8), indicating a
‘substantial’agreement (Landis and Koch 1977). A Cohen’s
ĸof 0.79 suggests that the RCA tool accurately estimates
riparian condition for medium-sized rivers where the vali-
dation occurred (Weber and Tucannon). However, small
streams with narrow bands of riparian vegetation and small
patches of land use may not have the spatial extent to be
resolved in 30 m datasets. In such settings, the RCA tool’s
accuracy is likely to be lower.
Fig. 6 Map showing riparian condition assessment (RCA) tool outputs across the select watersheds of the Columbia River Basin
Environmental Management

Fig. 7 Pie chart showing riparian condition assessment (RCA) tool outputs for all streams by select watersheds in the Columbia River Basin
Environmental Management

Discussion
Interpreting and Comparing Riparian Conditions
Between Regions
One should exercise caution when interpreting and com-
paring riparian condition results between Utah and the CRB
watersheds. The CRB watersheds of this study were not
randomly selected and therefore are not an accurate repre-
sentation of the larger CRB. In fact, the selected watersheds
represent some of the least developed portions of the CRB,
skewing the riparian condition assessment to reflect more
intact conditions than likely exist elsewhere in the basin. To
emphasize this point, if a Washington statewide analysis
were performed, including watersheds near densely popu-
lated Puget Sound, where many watersheds have been
converted to urban land uses and dense transportation
infrastructures, it is highly likely that the analysis would
have similar overall riparian condition to Utah. Conse-
quently, it is not surprising that 30% of the riparian areas in
Utah were classified as poor or very poor condition com-
pared to only 15% in the watersheds analyzed in the CRB,
which includes no major metropolitan areas, and that only
10% of the riparian areas in Utah compared to 31% in the
CRB were classified as intact.
Land Ownership Implications for Riparian
Management
Riparian management in the western U.S. is complicated by
the fact that most riparian acreage is privately controlled or
intermingled with other ownerships (Leonard et al. 1997).
For instance, while only 21% of the state of Utah is private,
66% of the unconfined valley bottoms are privately owned.
Similarly, in the CRB watersheds 41% of the total land is
private while 69% of the unconfined valley bottoms are
private (Fig. 10). Because of this disproportionate private
ownership of riparian areas, the involvement and coopera-
tion of private landowners, ranchers, and local, state, and
federal resource managers is critical to the success of
riparian management programs (Leonard et al. 1997).
Moreover, riparian areas under private ownership were
found to be in much poorer condition than publicly admi-
nistered land (Fig. 10). Specifically, in Utah 40% of pri-
vately owned riparian areas were in poor condition vs.
publicly administered lands that had only 14% of their
riparian areas in poor condition. In the CRB privately
owned riparian areas were found to have 21% poor vs.
publicly administered land with 4% poor condition. The
higher rates of degradation on private lands underscores the
need to engage with private landowners through agencies
such as the Natural Resources Conservation Service
(NRCS) and state fish and wildlife agencies (e.g. State
Departments of Natural Resources). These agencies can
provide landowners with financial and technical assistance
to help improve the condition of riparian areas on many
working range, forest, and farmlands.
Uses, Limitations, and Future Work
While higher resolution imagery (e.g., Macfarlane et al.
2016b) and LiDAR (e.g., Johansen et al. 2010) have been
successfully used to drive riparian vegetation classifications,
it is often prohibitively expensive to classify large water-
sheds at high resolutions (Salo et al. 2016). Moreover, such
inputs are not uniformly available across many parts of the
U.S. Consequently, our interest was in testing the model’s
capacity to produce accurate results using nationwide pub-
licly available, moderate resolution datasets. We found that
even when run with these moderate resolution datasets, the
RCA model produced riparian conditions that reasonably
approximated actual conditions, especially in areas where
transportation infrastructure, land-use intensity, and riparian
vegetation conversion are important factors. This finding is
similar to Lisenby and Fryirs (2017) who found that mod-
erate resolution data were appropriate for assessing sedi-
ment connectivity at the watershed scale.
We attribute our successful model outputs using
medium-resolution inputs, at least in part, to processing
Table 4 Summary of riparian condition assessment (RCA) tool
outputs for all streams by category in the Columbia River Basin
watersheds
Riparian condition
assessment
Stream length
(km)
% of drainage
network
Confined-impacted 2891 11
Confined-unimpacted 14,428 53
Very poor 71.5 <1
Poor 1390 5
Moderate 3332 12
Good 1986 7
Intact 3080 11
Total 27,179
Table 5 Summary of Utah statewide riparian condition assessment
(RCA) tool for partly confined and unconfined streams by category
Riparian condition
assessment
Stream length
(km)
% of drainage
network
Very poor 211.7 2
Poor 2839.4 28
Moderate 4108.4 41
Good 1917.9 19
Intact 1040.6 10
Total 10,118
Environmental Management

steps within our workflow that (1) aggregated land cover
classes into two broad categories (native and non-native/
upland) and (2) averaged condition values over 500 m
reaches. Studies show that classification accuracy greatly
increases when vegetation classes are lumped together (e.g.,
Driese et al. 2004). Nevertheless, the coarseness of the input
data resulted in output data limitations. There are at least
three limitations that are worth discussing: (1) 30 m land
cover classifications may be too coarse to consistently
capture narrow riparian areas, (2) 30 m land cover classifi-
cations may often misclassify invasive vegetation, and (3)
there is uncertainty in what historic vegetation existed and
at what levels of coverage.
Fig. 8 Pie chart showing riparian condition assessment (RCA) tool outputs for partly confined and unconfined streams by US Environmental
Protection Agency Level III Ecoregions in Utah
Table 6 Summary of the riparian condition assessment (RCA) tool
outputs for partly confined and unconfined streams by category in the
Columbia River Basin watersheds
Riparian condition
assessment
Stream length
(km)
% of drainage
network
Very poor 71.5 1
Poor 1390.5 14
Moderate 3332 34
Good 1986 20
Intact 3080 31
Total 9860
Environmental Management

In narrow, riparian corridors, 30 m spatial resolution data
appear to be too coarse to adequately capture riparian
condition (e.g., Congalton et al. 2002; Muller 1997).
Further, because riparian areas have steep environmental
gradients that produce many plant species within a short
distance, a given 30 m pixel may contain a mixture of
Fig. 9 Pie chart showing riparian condition assessment (RCA) tool outputs for partly confined and unconfined streams by select watersheds in the
Columbia River Basin
Environmental Management

several plant species in various proportions producing
“mixed pixels”(Zomer et al. 2009). As such, RCA outputs
created using 30 m data are more reliable in wider flood-
plain riparian ecosystems with larger homogeneous patches
of vegetation. In narrower riparian areas, higher resolution
inputs may be more appropriate (e.g., Macfarlane et al.
2016b), or an on-the-ground assessment may be necessary.
Fortunately, with minor modifications, the RCA tool can be
run with higher resolution input data. Higher spatial reso-
lution increases the number of “pure pixels”, thus removing
a large source of error (Zomer et al. 2009), allowing
for finer resolution outputs. Future work will focus
on running the RCA tool with higher resolution inputs
where available.
In the Colorado Plateau ecoregion of Utah, where
tamarisk is the dominant floodplain species (Nagler et al.
2011) land cover classifications derived from 30 m data
often fail to capture the full extent of tamarisk invasions.
This is especially true in narrow valley bottoms or gorges
where vegetation can be hard to accurately detect in 30 m
resolution satellite imagery due to shadows. We also attri-
bute this classification failure, at least in part, to large
swaths of tamarisk defoliated by the tamarisk leaf beetles.
The tamarisk beetle was released as a biological control
agent by the U.S. Department of Agriculture and since 2001
tamarisk leaf beetle have defoliated much of tamarisk in this
area (Bloodworth et al. 2016). In the LANDFIRE’s EVT
classification defoliated tamarisk are often misclassified as
upland classes, likely because these classes have low NDVI
(greenness) values similar to those of defoliated tamarisk
(Macfarlane et al. 2016b). As such, the RCA results indicate
intact and good condition for some of these rivers, yet these
Table 7 Error matrix and Cohen’s Kappa score for all assessed stream reaches illustrating the agreement of ground based to modeled floodplain
and riparian condition assessment
Field data RCA model output
Impacted Unimpacted Very poor Poor Moderate Good Intact Row total Producer accuracy Omission error
Impacted 11 11 100% 0%
Unimpacted 1 910 90% 10%
Very poor 21 3 67% 33%
Poor 18 2 20 90% 10%
Moderate 21 21 100% 0%
Good 6 12 18 67% 33%
Intact 2 79 78% 22%
Column total 12 9 2 19 29 14 7 92
Consumer accuracy 92% 100% 100% 95% 72% 86% 100%
Commission error 8% 0% 0% 5% 28% 14% 0%
Overall accuracy 87%
Cohen’sĸ0.87
The diagonal in bold text shows correctly modeled riparian condition
Table 8 Error matrix and
Cohen’s Kappa score illustrating
the agreement of ground based
to modeled floodplain and
riparian condition assessment
for partly confined and
unconfined reaches
Field data RCA model output
Very
poor
Poor Moderate Good Intact Row
total
Producer
accuracy
Omission
error
Very poor 21 3 67% 33%
Poor 18 2 20 90% 10%
Moderate 21 21 100% 0%
Good 6 12 18 67% 33%
Intact 2 79 78% 22%
Column total 2 19 29 14 7 71
Consumer
accuracy
100% 95% 72% 86% 100%
Commission error 0% 5% 28% 14% 0%
Overall accuracy 84%
Cohen’sĸ0.79
The diagonal in bold text shows correctly modeled riparian condition
Environmental Management

areas are dominated by tamarisk (see for example Colorado
and Green Rivers Fig. 4).
RVD scores, an important input to the RCA tool, depend
on how well the historic vegetation layer captures the his-
toric coverage of native riparian communities. The LAND-
FIRE BpS, which we used in this study, uses a predictive
modeling approach based on plot data and biophysical gra-
dient data layers, but does not incorporate imagery
(LANDFIRE 2016a). As such, historic vegetation data are
inherently coarser than existing vegetation data, which is
based on Landsat satellite imagery (LANDFIRE 2016a).
Despite this, the RCA model outputs still provide a reliable
indicator of riparian modification because the location and
extent of riparian vegetation are highly predictable (i.e.
adjacent to perennial waterways and in floodplains) and the
level of classification needed for model application is rela-
tively coarse (i.e. native vs. non-native riparian vegetation).
Despite precision and accuracy issues associated with
running the RCA tool using medium-resolution inputs, the
RCA tool outputs can be effectively applied to various river
and floodplain restoration and conservation planning
efforts. At the regional scale, RCA outputs can provide
meaningful contextual analyses of riparian condition
between watersheds. By revealing patterns of degradation,
such analyses provide critical information to resources
managers for prioritizing watershed conservation and
restoration efforts (e.g., Corsair et al. 2009). Specifically,
RCA outputs can be used to cost effectively identify areas
where restoration may be ineffective owing to high flood-
plain fragmentation (potential sacrifice areas), areas in need
of restoration that have the potential to transition toward
improved condition (O’Brien et al. 2017), or to prioritize
urban growth management and prevent encroachment on
relatively unimpacted floodplains. Once priority restoration
Fig. 10 Land ownership map showing percent ownership of the entire regions and for partly confined and unconfined valley bottoms along with
riparian condition assessment (RCA) tool outputs values by ownership type
Environmental Management

and conservation areas have been identified, targeted col-
lections of higher resolution land cover and land use clas-
sifications can be utilized in these priority areas if so
desired. This approach maximizes limited restoration
resources by limiting the collection of costly high-resolution
classifications to only where you are likely to “get the
greatest return on investment”.
Independent of watershed conservation and restoration
planning, RCA outputs can be used for modeling and
evaluating relationships between species that rely on ripar-
ian habitats for portions of their life cycles, and the condi-
tion of those riparian habitats (e.g., Decker et al. 2017). The
RCA tool maps how floodplains have been altered onto
drainage networks. Independently, each vegetation change,
human land use, and transportation infrastructure input used
in the RCA tool directly impacts riparian and aquatic spe-
cies life cycles and community structure. For example,
riparian birds and amphibians, as well as many fish species,
are negatively affected by transportation infrastructure
(Ficetola et al. 2009; Hennings and Edge 2003; Kaufmann
and Hughes 2006; Rieman et al. 1997), non-native riparian
vegetation (Kennedy et al. 2005; Miller et al. 2003), and
riparian land use (Blair 1996; Kauffman and Krueger 1984;
Martin and McIntyre 2007). Additionally, future work
could include pairing predictions of riparian condition with
data on hydrology (e.g., Lane et al. 2017; Wenger et al.
2010), water temperature (e.g., Isaak et al. 2016; McNyset
et al. 2015), and geomorphic setting and context (Beechie
et al. 2013; Kasprak et al. 2016; Wheaton et al. 2015)to
conceptually understand factors that impact biological
communities across river networks.
In this application, our primary intent was to develop a
consistent regional analysis of riparian floodplain condition.
Our selected indicators of riparian floodplain health, RVD,
land use intensity, and floodplain fragmentation were well
suited for this application because they could be con-
sistently “mapped”using freely available, region-wide data.
Hydrologic alterations are another important riparian con-
dition stressor. For instance, in the Colorado River basin
water withdrawals from dams and diversions reduce the
magnitude, duration, and frequency of floods, which often
leads to dense thickets of tamarisk along floodplains fol-
lowed by rapid accretion of sediment on floodplains,
resulting in channel narrowing (Dean and Schmidt 2011;
Manners et al. 2014). Yet, hydrologic alteration stressors
were not assessed in this analysis because dam and diver-
sion data are difficult to use and are not regionally con-
sistency and/or available. Nevertheless, this does not
preclude the use of such data in future applications of the
RCA tool because the FIS framework is expandable and can
be modified to include additional inputs when and where
available. In priority watersheds, where funding has allowed
us to collect and analyze a suite of additional stressor data,
we have developed more comprehensive watershed scale
condition assessments (O’Brien et al. 2017). We plan to
continue to expand the RCA tool to produce more com-
prehensive riparian condition assessments by including
additional riparian stressors in watersheds where funding
and data are available.
In an effort to examine riparian condition change over
even broader spatial and temporal scales, we plan to run the
RCA tool as a time-varying dynamical model over large
areas such as the entire western U.S. To accomplish this, we
will use Google Earth Engine in a similar fashion to Don-
nelly et al. (2016) and vary vegetation and land use inputs
through time using historic Landsat imagery derivatives. If
the model were to be run in this manner, the outputs might
help measure the effectiveness of restoration actions or
natural flow and climatic variability. Ideally, a time-step
version of the RCA will elucidate informative patterns
associated with urban development, agriculture, vegetation
community shifts due to disturbance (e.g. timber harvest,
fire, etc.) and impacts like browse pressure (e.g. from bea-
ver, cattle, elk, etc.).
Conclusions
Effectively managing stream and river ecosystems requires
comprehensive and accurate riparian condition data on how
multiple stressors can affect floodplains. The results of the
newly developed drainage network-based model that we
present here provides one of the first major riparian con-
ditions assessments across large areas of the interior western
U.S. We found that the watersheds of Utah and the interior
CRB were ideal settings within which to develop and test
our floodplain condition assessment tool due to the diverse
climate, disturbance regimes and land use histories of these
regions. We also found that across our study watersheds,
riparian condition is highly variable, and is often impacted
by a combination of the multiple stressors we examined.
Even when using relatively coarse input data, our con-
dition assessment provides critical information regarding
the extent to which riparian areas remain intact or have been
degraded. As such, these data can enhance river and
floodplain restoration and conservation planning by allow-
ing resource managers to identify the causes of riparian
degradation, prioritize watersheds for conservation, target
areas in need of restoration, and identify areas where
restoration and conservation may be ineffective due to land
use constraints. Although we were able to identify how
land-use intensity, vegetation change, and valley bottom
infrastructure impact floodplains across Utah and the CRB,
spatially explicit, multi-stressor assessments simply do not
exist for much of the world. Fortunately, the framework on
which the model is built provides a foundation for broad
Environmental Management

applications elsewhere in the world where sufficient input
data exist or can be collected. Moreover, the techniques are
scalable to entire regions and/or could be run in smaller
regions with higher resolution inputs.
Data Availability
We generated spatial data layers to enable resource man-
agement agencies, restoration practitioners, and other inter-
ested parties to access and use RCA data to inform their
management decisions. The outputs of this work are publicly
available at: http://rcat.riverscapes.xyz and the source code
of the Riparian Condition Assessment Toolbox (R-CAT) is
available at: https://github.com/Riverscapes/RCAT.
Acknowledgements This work was supported by U.S. Department of
the Interior Bureau of Land Management (USU Award No. 151010),
Utah Department of Natural Resources’Endangered Species Mitiga-
tion Fund (USU Award No. 140600), Utah Division of Wildlife
Resources’Pittman and Robertson Fund (USU Award No. 150736),
Snake River Salmon Recovery Board through Eco Logical Research
(USU Award No. 200239) and Bonneville Power Administration
(BPA project numbers: CHaMP 2011-006 and ISEMP 2013-017), as
part of the Columbia Habitat Monitoring Program (http://champmo-
nitoring.org) through a sub-award from Eco Logical Research (USU
Award No. 150737). We are grateful to Justin Jimenez (BLM) who
had the vision to undertake a riparian assessment across the Colorado
Plateau, and built the partnerships for successful implementation.
Model development benefitted greatly from insights and conversations
with Jeremy Jarnecke (BLM), Russell Norvell (UDWR), Jimi Gragg
(UDWR), Chris Keleher (UDNR), Frank Howe (USU), Justin Shan-
non (UDWR), Gary O’Brien (USU), Phaedra Budy (USGS/USU),
Konrad Hafen (USU), Nick Bouwes (USU), Chris Jordan (NOAA),
and the Weber River Watershed Partnership (UT). Adan Banda,
Micael Albonico, Shane Hill, Martha Jensen, Matt Meier, and Chris
Smith provided GIS support. Reid Camp, Andrew Hill, and Scott
Shahverdian provided field-validation support. We thank two anon-
ymous reviewers and Angus Webb for their review comments that
significantly improved this paper.
Compliance with Ethical Standards
Conflict of interest The authors declare that they have no conflict of
interest.
References
Adriaenssens V, Baets BD, Goethals PLM, Pauw ND (2003) Fuzzy
rule-based models for decision support in ecosystem manage-
ment. Sci Total Environ 319:1–12
Allan JD (2004) Landscapes and riverscapes: the influence of
land use on stream ecosystems. Annu Rev Ecol Evol Syst
35:257–284
Baron JS et al. (2003) Sustaining healthy freshwater ecosystems.
Issues Ecol 10:1–16
Beechie T et al. (2013) Restoring salmon habitat for a changing cli-
mate. River Res Appl 29:939–960
Beschta RL, Taylor RL (1988) Stream temperature increases and land
use in a forested Oregon watershed JAWRA. J Am Water Resour
Assoc 24:19–25. https://doi.org/10.1111/j.1752-1688.1988.
tb00875.x
Blair RB (1996) Land use and avian species diversity along an urban
gradient. Ecol Appl 6:506–519
Blanton P, Marcus WA (2013) Transportation infrastructure, river
confinement, and impacts on floodplain and channel habitat,
Yakima and Chehalis rivers, Washington, USA. Geomorphology
189:55–65
Bloodworth BR, Shafroth PB, Sher AA, Manners RB, Bean DW,
Johnson MJ, Hinojosa-Huerta O (2016) Tamarisk beetle (Dior-
habda spp.) in the Colorado River basin: synthesis of an expert
panel forum. Scientific and Technical Report, Grand Junction,
CO
Booth DB, Jackson CR (1997) Urbanization of aquatic systems:
degradation thresholds, stormwater detection, and the limits of
mitigation JAWRA. J Am Water Resour Assoc 33:1077–1090.
https://doi.org/10.1111/j.1752-1688.1997.tb04126.x
Bouwes N et al. (2011) Scientific protocol for salmonid habitat surveys
within the Columbia Habitat Monitoring Program. Integrated
Status and Effectiveness Monitoring Program, Wauconda, WA
Carpenter SR, Caraco NF, Correll DL, Howarth RW, Sharpley AN,
Smith VH (1998) Nonpoint pollution of surface waters with
phosphorus and nitrogen. Ecol Appl 8:559–568. https://doi.org/
10.1890/1051-0761(1998)008[0559:NPOSWW]2.0.CO;2
Castellarin A, Di Baldassarre G, Brath A (2011) Floodplain manage-
ment strategies for flood attenuation in the river Po. River Res
Appl 27:1037–1047
Comer P et al. (2003) Ecological systems of the United States: a
working classification of US terrestrial systems. NatureServe,
Arlington, VA
Congalton RG (1991) A review of assessing the accuracy of classifi-
cations of remotely sensed data. Remote Sens Environ 37:35–46.
https://doi.org/10.1016/0034-4257(91)90048-B
Congalton RG, Birch K, Jones R, Schriever J (2002) Evaluating
remotely sensed techniques for mapping riparian vegetation.
Comput Electron Agric 37:113–126
Corsair H, Ruch JB, Zheng PQ, Hobbs BF, Koonce JF (2009) Mul-
ticriteria decision analysis of stream restoration: potential and
examples. Group Decis Negot 18:387–417
Costanza R et al. (2016) The value of the world’s ecosystem services
and natural capital (1997). The Globalization and Environment
Reader 117
Dean DJ, Schmidt JC (2011) The role of feedback mechanisms in
historic channel changes of the lower Rio Grande in the Big Bend
region. Geomorphology 126:333–349
Decker KL, Pocewicz A, Harju S, Holloran M, Fink MM, Toombs TP,
Johnston DB (2017) Landscape disturbance models consistently
explain variation in ecological integrity across large landscapes.
Ecosphere 8:e01775-n/a. https://doi.org/10.1002/ecs2.1775.
DeFries RS, Foley JA, Asner GP (2004) Land‐use choices: balancing
human needs and ecosystem function. Front Ecol Environ
2:249–257
DeLaney TA (1995) Benefits to downstream flood attenuation and
water quality as a result of constructed wetlands in agricultural
landscapes. J Soil Water Conserv 50:620–626
Donnelly JP, Naugle DE, Hagen CA, Maestas JD (2016) Public lands
and private waters: scarce mesic resources structure land tenure
and sage-grouse distributions. Ecosphere 7:e01208–e01208.
https://doi.org/10.1002/ecs2.1208
Driese KL, Reiners WA, Lovett GM, Simkin SM (2004) A vegetation
map for the Catskill Park, NY, derived from multi-temporal
Landsat imagery and GIS data. Northeast Nat 11:421–442
Dudgeon D et al. (2006) Freshwater biodiversity: importance, threats,
status and conservation challenges. Biol Rev 81:163–182
Esri (2016a) ArcGIS. Redlands, CA
Esri (2016b) GIS dictionary: Thiessen polyons. Esri. http://support.
esri.com/en/knowledgebase/GISDictionary/term/Thiessen%
20polygons (Accessed March 2016)
Environmental Management

Fahrig L (2003) Effects of habitat fragmentation on biodiversity. Annu
Rev Ecol Evol Syst 34:487–515
Ficetola GF, Padoa-Schioppa E, De Bernardi F (2009) Influence of
landscape elements in riparian buffers on the conservation of
semiaquatic amphibians. Conserv Biol 23:114–123
Foley JA et al. (2005) Global consequences of land use. Science
309:570–574
Forman R et al. (2002) Road ecology. Science and solutions. Island
Press, Washington. DC
Fryirs KA, Brierley GJ (2013) Geomorphic analysis of river systems:
an approach to reading the landscape. Wiley-Blackwell, Hobo-
ken, NJ
Fryirs KA, Wheaton JM, Brierley GJ (2016) An approach for mea-
suring confinement and assessing the influence of valley setting
on river forms and processes. Earth Surf Process Landf
41:701–710
Gallant JC, Dowling TI (2003) A multiresolution index of valley
bottom flatness for mapping depositional areas. Water Resour Res
39. https://doi.org/10.1029/2002WR001426
Galloway JN et al. (2004) Nitrogen cycles: past, present, and future.
Biogeochemistry 70:153–226. https://doi.org/10.1007/s10533-
004-0370-0
Gesch D, Evans G, Mauck J, Hutchinson J, Carswell Jr WJ (2009) The
national map—elevation US Geological Survey. National Geos-
patial Technical Operations Center
Gilbert JT, Macfarlane WW, Wheaton JM (2016) V-BET: a GIS tool
for delineating valley bottoms across entire drainage networks.
Comput Geosci 97:1–14
Goetz SJ (2006) Remote sensing of riparian buffers: past progress and
future prospects JAWRA. J Am Water Resour Assoc
42:133–143. https://doi.org/10.1111/j.1752-1688.2006.tb03829.x
Golet GH et al. (2008) Wildlife response to riparian restoration on the
Sacramento River. San Francisco Estuary Watershed Sci 2:1–26
Gren M, Groth K-H, Sylvén M (1995) Economic values of Danube
floodplains. J Environ Manag 45:333–345
Grossman D et al. (1998) International classification of ecological
communities: terrestrial vegetation of the United States. The
Nature Conservancy, Arlington, VA
Gurnell AM, Gregory KJ, Petts GE (1995) The role of coarse woody
debris in forest aquatic habitats: implications for management
aquatic conservation: marine and freshwater. Ecosystems
5:143–166. https://doi.org/10.1002/aqc.3270050206
Hall JE, Holzer DM, Beechie TJ (2007) Predicting river floodplain and
lateral channel migration for salmon habitat conservation
JAWRA. J Am Water Resour Assoc 43:786–797. https://doi.org/
10.1111/j.1752-1688.2007.00063.x
Harris R, Olson C (1997) Two-stage system for prioritizing riparian
restoration at the stream reach and community scales. Restor Ecol
5:34–42
Hennings LA, Edge WD (2003) Riparian bird community structure in
Portland, Oregon: habitat, urbanization, and spatial scale patterns.
Condor 105:288–302
Hooper DU et al. (2005) Effects of biodiversity on ecosystem func-
tioning: a consensus of current knowledge. Ecol Monogr 75:3–35
Hough-Snee N, Roper BB, Wheaton JM, Budy P, Lokteff RL (2013)
Riparian vegetation communities change rapidly following pas-
sive restoration at a northern Utah stream. Ecol Eng 58:371–377.
https://doi.org/10.1016/j.ecoleng.2013.07.042
Isaak D et al. (2016) NorWeST modeled summer stream temperature
scenarios for the western US. US Forest Service, Rocky Moun-
tain Research Station Research Data Archive.https://doi.org/10.
2737/RDS-2016-0033
Jeffres CA, Opperman JJ, Moyle PB (2008) Ephemeral floodplain
habitats provide best growth conditions for juvenile Chinook
salmon in a California river. Environ Biol Fish 83:449–458
Johansen K, Arroyo LA, Armston J, Phinn S, Witte C (2010) Mapping
riparian condition indicators in a sub-tropical savanna environ-
ment from discrete return LiDAR data using object-based image
analysis. Ecol Indic 10:796–807. https://doi.org/10.1016/j.
ecolind.2010.01.001
Johansen K, Phinn S (2006) Linking riparian vegetation spatial
structure in Australian tropical savannas to ecosystem health
indicators: semi-variogram analysis of high spatial resolution
satellite imagery Canadian. J Remote Sens 32:228–243. https://
doi.org/10.5589/m06-020
Kasprak A et al. (2016) The blurred line between form and process: a
comparison of stream channel classification frameworks. PLoS
ONE 11:e0150293. https://doi.org/10.1371/journal.pone.0150293
Kauffman JB, Beschta RL, Otting N, Lytjen D (1997) An ecological
perspective of riparian and stream restoration in the western
United States. Fisheries 22:12–24
Kauffman JB, Krueger WC (1984) Livestock impacts on riparian
ecosystems and streamside management implications... a review.
J Range Manag 37:430–438
Kaufmann PR, Hughes RM (2006) Geomorphic and anthropogenic
influences on fish and amphibians in Pacific Northwest coastal
streams. In: American Fisheries Society Symposium, vol 429.
Bethesda, MD, p 55
Kennedy TA, Finlay JC, Hobbie SE (2005) Eradication of invasive
Tamarix ramosissima along a desert stream increases native fish
density. Ecol Appl 15:2072–2083
Klemas V (2014) Remote sensing of riparian and wetland buffers: an
overview. J Coast Res 30:869–880. https://doi.org/10.2112/
JCOASTRES-D-14-00013.1
Klir GJ, Yuan B (1995) Fuzzy sets and Fuzzy logic: theory and
applications. Prentice Hall, Upper Saddle River, NJ
Kus BE (1998) Use of restored riparian habitat by the endangered
Least Bell's Vireo (Vireo bellii pusillus). Restor Ecol 6:75–82
LANDFIRE (2016a) Biophysical Settting (BPS) layer Landscape Fire
and Resource Management Planning Tools project. http://www.la
ndfire.gov/NationalProductDescriptions20.php (accessed March
2016)
LANDFIRE (2016b) Existing Vegetation Type (EVT) layer Land-
scape Fire and Resource Management Planning Tools project.
http://www.landfire.gov/NationalProductDescriptions21.php
(accessed April 2016)
Landis JR, Koch GG (1977) The measurement of observer agreement
for categorical data. Biometrics 33:159–174
Lane BA, Dahlke HE, Pasternack GB, Sandoval‐Solis S (2017)
Revealing the diversity of natural hydrologic regimes in Cali-
fornia with relevance for environmental flows applications
JAWRA. J Am Water Resour Assoc 53:411–430
Leonard S, Elsbernd V, Borman MM, Swanson S, Kinch G (1997)
Grazing management for riparian-wetland areas. US Department
of the Interior, Bureau of Land Management, National Applied
Resource Sciences Center, Denver, CO
Liess M, Schulz R (1999) Linking insecticide contamination and
population response in an agricultural stream. Environ Toxicol
Chem 18:1948–1955. https://doi.org/10.1002/etc.5620180913
Lisenby PE, Fryirs KA (2017) ‘Out with the Old?’Why coarse spatial
datasets are still useful for catchment-scale investigations of
sediment (dis) connectivity. Earth Surf Process Landf 40
(10):1588–1596
Lowrance R (1998) Riparian forest ecosystems as filters for nonpoint-
source pollution. In: Pace ML et al (eds) Successes, limitations,
and frontiers in ecosystem science, Springer, New York, p
113–141
Lowrance R et al. (1997) Water quality functions of riparian forest
buffers in Chesapeake Bay watersheds. Environ Manag
21:687–712
Environmental Management

Macfarlane WW, Gilbert JT, Jensen ML, Gilbert JD, Hough-Snee N,
McHugh PA, Wheaton JM (2016a) Riparian vegetation as an
indicator of riparian condition: detecting departures from historic
condition across the North American West. J Environ Manag
202:447–460
Macfarlane WW, McGinty CM, Laub BG, Gifford SJ (2016b) High-
resolution riparian vegetation mapping to prioritize conservation
and restoration in an impaired desert river. Restor Ecol. https://
doi.org/10.1111/rec.12425
Mander Ü, Hayakawa Y, Kuusemets V (2005) Purification processes,
ecological functions, planning and design of riparian buffer zones
in agricultural watersheds. Ecol Eng 24:421–432
Manners RB, Schmidt JC, Scott ML (2014) Mechanisms of
vegetation-induced channel narrowing of an unregulated canyon
river: results from a natural field-scale experiment. Geomor-
phology 211:100–115
Martin TG, McIntyre S (2007) Impacts of livestock grazing and tree
clearing on birds of woodland and riparian habitats. Conserv Biol
21:504–514
Mathworks (2017) Defuzzification methods: centroid. Mathworks.
https://www.mathworks.com/help/fuzzy/examples/defuzzifica
tion-methods.html?requestedDomain=www.mathworks.
com#zmw57dd0e2454. (Accessed 2/27/17 2017)
May CW, Horner RR, Karr JR, Mar BW, Welch EB (1999) Effects of
urbanization on small streams in the Puget Sound ecoregion.
Watershed Prot Tech 2:79
McNamara JP, Ziegler AD, Wood SH, Vogler JB (2006) Channel head
locations with respect to geomorphologic thresholds derived from
a digital elevation model: a case study in northern Thailand. For
Ecol Manag 224:147–156. https://doi.org/10.1016/j.foreco.2005.
12.014
McNyset KM, Volk CJ, Jordan CE (2015) Developing an effective
model for predicting spatially and temporally continuous stream
temperatures from remotely sensed land surface temperatures.
Water 7:6827–6846
Miller JR, Wiens JA, Hobbs NT, Theobald DM (2003) Effects of
human settlement on bird communities in lowland riparian areas
of Colorado (USA). Ecol Appl 13:1041–1059
Montgomery DR (2001) Slope distributions, threshold hillslopes, and
steady-state topography. Am J Sci 301:432–454. https://doi.org/
10.2475/ajs.301.4-5.432
Montgomery DR,(2002) Valley formation by fluvial and glacial ero-
sion Geology 30:1047–1050. https://doi.org/10.1130/0091-7613
(2002)030<1047:VFBFAG>2.0.CO;2
Muller E (1997) Mapping riparian vegetation along rivers: old con-
cepts and new methods. Aquat Bot 58:411–437
Nagler PL, Glenn EP, Jarnevich CS, Shafroth PB (2011) Distribution
and abundance of saltcedar and Russian olive in the western
United States. Crit Rev Plant Sci 30:508–523
Naiman RJ, Decamps H (1997) The ecology of interfaces: riparian
zones. Annu Rev Ecol Syst 28:621–658
Naiman RJ, Bilby RE, Bisson PA (2000) Riparian ecology and
management in the Pacific coastal rain forest. Bioscience
50:996–1011
Nardi F, Vivoni ER, Grimaldi S (2006) Investigating a floodplain
scaling relation using a hydrogeomorphic delineation method
Water Resour Res 42. https://doi.org/10.1029/2005WR004155
O'Brien G et al. (in revision) A rapid and reliable method for mod-
elling valley confinement and differentiating valley setting across
entire drainage networks. Earth Surf Process Landf
O’Brien GR, Wheaton J, Fryirs K, McHugh P, Bouwes N, Brierley G,
Jordan C (2017) A geomorphic assessment to inform strategic
stream restoration planning in the Middle Fork John Day
Watershed, Oregon, USA. J Maps 13:369–381
Openshaw S (1996) Fuzzy logic as a new scientific paradigm for doing
geography. Environ Plan A 28:761–768
Opperman JJ, Galloway GE, Fargione J, Mount JF, Richter BD,
Secchi S (2009) Sustainable floodplains through large-scale
reconnection to rivers. Science 326:1487–1488
Ormerod SJ, Dobson M, Hildrew AG, Townsend CR (2010) Multiple
stressors in freshwater ecosystems. Freshw Biol 55:1–4. https://
doi.org/10.1111/j.1365-2427.2009.02395.x
Pimentel D, Lach L, Zuniga R, Morrison D (2000) Environmental and
economic costs of nonindigenous species in the United States.
Bioscience 50:53–65
Poff N, Brinson MM, Day J (2002) Aquatic ecosystems and global
climate change. Pew Center on Global Climate Change, Arling-
ton, VA, 44
Rieman BE, Lee DC, Thurow RF (1997) Distribution, status, and
likely future trends of bull trout within the Columbia River and
Klamath River basins. North Am J Fish Man 17:1111–1125
Rollins MG (2009) LANDFIRE: a nationally consistent vegetation,
wildland fire, and fuel assessment. Int J Wildland Fire
18:235–249
Rolls RJ, Arthington AH (2014) How do low magnitudes of hydro-
logic alteration impact riverine fish populations and assemblage
characteristics? Ecol Indic 39:179–188. https://doi.org/10.1016/j.
ecolind.2013.12.017
Roon DA, Wipfli MS, Wurtz TL (2014) Effects of invasive European
bird cherry (Prunus padus) on leaf litter processing by aquatic
invertebrate shredder communities in urban Alaskan streams.
Hydrobiologia 736:17–30
Roon DA, Wipfli MS, Wurtz TL, Blanchard AL (2016) Invasive
European bird cherry (Prunus padus) reduces terrestrial prey
subsidies to urban Alaskan salmon streams. Can J Fish Aquat Sci
73:1679–1690
Royan A, Prudhomme C, Hannah DM, Reynolds SJ, Noble DG,
Sadler JP (2015) Climate-induced changes in river flow regimes
will alter future bird distributions. Ecosphere 6:1–10. https://doi.
org/10.1890/ES14-00245.1
Salo JA, Theobald DM, Brown TC (2016) Evaluation of methods for
delineating riparian zones in a semi-arid montane watershed.
JAWRA J Am Water Resour Assoc 52:632–647. https://doi.org/
10.1111/1752-1688.12414
Schoonover JE, Lockaby BG, Pan S (2005) Changes in chemical and
physical properties of stream water across an urban-rural gradient
in western Georgia Urban. Ecosystems 8:107–124. https://doi.
org/10.1007/s11252-005-1422-5
Schorghofer N, Rothman DH (2002) Acausal relations between
topographic slope and drainage area. Geophys Res Lett 29.
https://doi.org/10.1029/2002GL015144
Shafroth PB, Stromberg JC, Patten DT (2002) Riparian vegetation
response to altered disturbance and stress regimes. Ecol Appl
12:107–123
Stella J, Rodríguez-González P, Dufour S, Bendix J (2013) Riparian
vegetation research in Mediterranean-climate regions: common
patterns, ecological processes, and considerations for manage-
ment. Hydrobiologia 719:291–315. https://doi.org/10.1007/
s10750-012-1304-9
Stohlgren TJ, Bull KA, Otsuki Y, Villa CA, Lee M (1998) Riparian
zones as havens for exotic plant species in the central grasslands.
Plant Ecol 138:113–125
Stromberg JC et al. (2007) Altered stream‐flow regimes and invasive
plant species: the tamarix case. Glob Ecol Biogeogr 16:381–393
Tabacchi E, Lambs L, Guilloy H, Planty-Tabacchi AM, Muller E,
Decamps H (2000) Impacts of riparian vegetation on hydrological
processes. Hydrol Process 14:2959–2976
Taniguchi KT, Biggs TW (2015) Regional impacts of urbanization on
stream channel geometry: a case study in semiarid southern
California. Geomorphology 248:228–236. https://doi.org/10.
1016/j.geomorph.2015.07.038
Environmental Management

Tockner K, Stanford JA (2002) Riverine flood plains: present state and
future trends. Environ Conserv 29:308–330
Tucker GE, Bras RL (1998) Hillslope processes, drainage density, and
landscape morphology. Water Resour Res 34:2751–2764. https://
doi.org/10.1029/98WR01474
US Census Bureau (2016) TIGER Road data. https://www.census.gov/
geo/maps-data/data/tiger.html (accessed April 2016)
USGS (2016) National Hydrography Dataset. US Geological Survey.
http://nhd.usgs.gov/ (accessed 6 June-July 2016)
Vigil JF, Pike RJ, Howell DG (2000) A tapestry of time and terrain.
US Geological Survey. http://ulpeis.anl.gov/documents/dpeis/
references/pdfs/USGS_2003.pdf (accessed April 2016)
Vitousek PM, Mooney HA, Lubchenco J, Melillo JM (1997) Human
domination of Earth's ecosystems. Science 277:494–499
Ward J, Tockner K, Schiemer F (1999) Biodiversity of floodplain river
ecosystems: ecotones and connectivity Regulated Rivers. Res
Manag 15:125–139
Wenger SJ, Luce CH, Hamlet AF, Isaak DJ, Neville HM (2010)
Macroscale hydrologic modeling of ecologically relevant flow
metrics. Water Resour Res 46. https://doi.org/10.1029/
2009WR008839.
Wheaton JM, Fryirs KA, Brierley G, Bangen SG, Bouwes N, O'Brien
G (2015) Geomorphic mapping and taxonomy of fluvial land-
forms. Geomorphology 248:273–295. https://doi.org/10.1016/j.
geomorph.2015.07.010
Wheaton JM et al. (2017) Upscaling site-scale ecohydraulic models to
inform salmonid population‐level life cycle modelling and
restoration actions—lessons from the Columbia River Basin.
Earth Surf Process Landf. https://doi.org/10.1002/esp.4137
Willgoose G, Bras RL, Rodriguez-Iturbe I (1991) A physical expla-
nation of an observed link area‐slope relationship. Water Resour
Res 27:1697–1702. https://doi.org/10.1029/91WR00937
Woltemade CJ, Potter KW (1994) A watershed modeling analysis of
fluvial geomorphologic influences on flood peak attenuation.
Water Resour Res 30:1933–1942
Zadeh LA (1996) Fuzzy logic=computing with words. IEEE Trans
Fuzzy Syst 4:103–111
Zomer RJ, Trabucco A, Ustin SL (2009) Building spectral libraries for
wetlands land cover classification and hyperspectral remote sen-
sing. J Environ Manag 90:2170–2177. https://doi.org/10.1016/j.
jenvman.2007.06.028
Environmental Management